Abstract
Background: In this study, an automatic scoring system for the functional movement screen (FMS) was developed. Methods: Thirty healthy adults fitted with full-body inertial measurement unit sensors completed six FMS exercises. The system recorded kinematics data, and a professional athletic trainer graded each participant. To reduce the number of input variables for the predictive model, ordinal logistic regression was used for subset feature selection. The ensemble learning algorithm AdaBoost.M1 was used to construct classifiers. Accuracy and F score were used for classification model evaluation. The consistency between automatic and manual scoring was assessed using a weighted kappa statistic. Results: When all the features were used, the predict model presented moderate to high accuracy, with kappa values between fair to very good agreement. After feature selection, model accuracy decreased about 10%, with kappa values between poor to moderate agreement. Conclusions: The results indicate that higher prediction accuracy was achieved using the full feature set compared with using the reduced feature set.
Highlights
The functional movement screen (FMS) is a screening tool widely used by sports medicine practitioners to evaluate fundamental movement patterns in competitive athletes at risk of but not currently experiencing signs or symptoms of musculoskeletal injury
The purpose of the present study was to develop a boosting ensemble machine learning method, in which score and range of motion (ROM) are the dependent and independent variables, that assesses movement dysfunction by automatically detecting movement deviations during the FMS test from ROM data collected by inertial measurement unit (IMU) sensors
Differences in scores were within 1 point, ranging from 0.04 to 0.12
Summary
The functional movement screen (FMS) is a screening tool widely used by sports medicine practitioners to evaluate fundamental movement patterns in competitive athletes at risk of but not currently experiencing signs or symptoms of musculoskeletal injury. Similar to other fitness tests, the FMS must be manually administered and scored by professionally trained individuals [5], challenging its wide implementation in gyms, sports studios, and other settings. Inertial measurement unit (IMU) sensors were used to record the movement data of participants performing six FMS tests. An automatic scoring system for the functional movement screen (FMS) was developed. Methods: Thirty healthy adults fitted with full-body inertial measurement unit sensors completed six FMS exercises. Accuracy and F score were used for classification model evaluation. Results: When all the features were used, the predict model presented moderate to high accuracy, with kappa values between fair to very good agreement. Model accuracy decreased about 10%, with kappa values between poor to moderate agreement. Conclusions: The results indicate that higher prediction accuracy was achieved using the full feature set compared with using the reduced feature set
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